No abstract

Bees provide important pollination services that maintain native plant populations and ecosystem resilience, which is critical to the conservation of the rich and endemic biodiversity of Kaya forests along the Kenyan Coast. This study examined bee composition and floral resources from the forest core to the surrounding farmlands around Kaya Muhaka forest. In total, 755 individual bees, representing 41 species from three families were recorded: Apidae, Halictidae and Megachilidae. Overall, Apidae were the most abundant with a proportion of 76% of the total bee individuals, Halictidae at 14% and Megachilidae at 10%. Bee composition was similar between forest edge and crop fields as compared to forest core and fallow farmlands. We found a significant decrease in bee diversity with increasing distance from the forest to the surrounding farming area. A high abundance of bees was recorded in fallow farmland, which could be explained by the high abundance of floral resources in the habitat. We found floral resources richness to significantly affect bee species richness. These findings are important for understanding the effects of land use change on insect pollinators and their degree of resilience in disturbed habitats.

Arabuko Sokoke Forest is the largest remaining single block of indigenous dry coastal tropical forest in Eastern Africa. Households within a 5 km buffer zone depend heavily communaut e locale a des projets de conservation sembleêtre un revenu durable et la satisfaction des besoins de base du m enage.

The COVID-19 pandemic has had a significant impact on the tourism industry, leading to global economic and societal disruptions, and a growing risk of a global recession. This project aimed to investigate the impact of the pandemic on conservation, communities, and businesses in Masai Mara, and identify critical factors for sustainable tourism recovery. Four objectives were explored: (1) awareness of critical factors for tourism recovery and sustainability during and after the pandemic period; (2) socio-economic vulnerabilities of indigenous communities to COVID-19; (3) lessons learned to enhance adaptation and resilience; and (4) the impact of COVID-19 on conservation management of the destination. We used mixed methods, including field observations, key informant interviews, and focus group discussions, to collect data from tourism industry businesses and policymakers in the Masai Mara conservation area. The findings indicated a negative large-scale effect on conservation, tourism business, and communities in the area. The study recommends integrated interventions by both county and national governments, targeting small, medium, and micro enterprises. The persistence of the economic damage to the tourism sector will depend on how both county and national governments handle policy interventions towards the funding of tourism SMMEs, the community livelihood programme, and conservation partnerships to incentivize tourism recovery.

The distribution of species is strongly influenced by habitat quality and its changes over time. Climate change has been identified as one of the major drivers of habitat loss, threatening the survival of many range-restricted animal species. Identification of spatiotemporal hotspots of species occurrence is important for understanding basic ecological processes particularly for the conservation of species at risk. This study models the spatiotemporal distribution of Rothschild's giraffe (Giraffa camelopardalis rothschildi) with the view of explaining the possible effects of changing habitat suitability in Kenya and across Africa. The study analyzes the relative importance of different climatic variables and establishes the variables that are the strongest predictors of the species' geographic range. We apply species distribution modelling to predict the species' response to future climate and land use change scenarios. Our model is based on occurrence data from the Global Biodiversity Information Facility (GBIF) for the period 1923-2019 and climatic data from the WorldClim. We fit the model using the Maximum Entropy (Maxent) algorithm to identify the combination of environmental responses, which best predicts evolving hotspots of occurrence for this species and future habitat suitability in face of climate change. The study demonstrates the usability of occurrence data over time on Rothschild's giraffe and gives insights on the integration of land use variables to be able to link species distribution patterns, land use change and climate change to effectively inform conservation management. ‡ §